def __init__()

in baselines/acktr/kfac.py [0:0]

• 39 lines of code
• 3 McCabe index (conditional complexity)

    def __init__(self, learning_rate=0.01, momentum=0.9, clip_kl=0.01, kfac_update=2, stats_accum_iter=60, full_stats_init=False, cold_iter=100, cold_lr=None, is_async=False, async_stats=False, epsilon=1e-2, stats_decay=0.95, blockdiag_bias=False, channel_fac=False, factored_damping=False, approxT2=False, use_float64=False, weight_decay_dict={},max_grad_norm=0.5):
        self.max_grad_norm = max_grad_norm
        self._lr = learning_rate
        self._momentum = momentum
        self._clip_kl = clip_kl
        self._channel_fac = channel_fac
        self._kfac_update = kfac_update
        self._async = is_async
        self._async_stats = async_stats
        self._epsilon = epsilon
        self._stats_decay = stats_decay
        self._blockdiag_bias = blockdiag_bias
        self._approxT2 = approxT2
        self._use_float64 = use_float64
        self._factored_damping = factored_damping
        self._cold_iter = cold_iter
        if cold_lr == None:
            # good heuristics
            self._cold_lr = self._lr# * 3.
        else:
            self._cold_lr = cold_lr
        self._stats_accum_iter = stats_accum_iter
        self._weight_decay_dict = weight_decay_dict
        self._diag_init_coeff = 0.
        self._full_stats_init = full_stats_init
        if not self._full_stats_init:
            self._stats_accum_iter = self._cold_iter

        self.sgd_step = tf.Variable(0, name='KFAC/sgd_step', trainable=False)
        self.global_step = tf.Variable(
            0, name='KFAC/global_step', trainable=False)
        self.cold_step = tf.Variable(0, name='KFAC/cold_step', trainable=False)
        self.factor_step = tf.Variable(
            0, name='KFAC/factor_step', trainable=False)
        self.stats_step = tf.Variable(
            0, name='KFAC/stats_step', trainable=False)
        self.vFv = tf.Variable(0., name='KFAC/vFv', trainable=False)

        self.factors = {}
        self.param_vars = []
        self.stats = {}
        self.stats_eigen = {}